Low-Complexity Compressive Channel Estimation for IRS-Aided mmWave Systems With Hypernetwork-Assisted LAMP Network
Journal
IEEE Communications Letters
Journal Volume
26
Journal Issue
8
Pages
1883
Date Issued
2022-08-01
Author(s)
Abstract
Intelligent reflecting surface (IRS) can enhance the wireless communication environment by smartly reflecting the incident signal toward desired directions. However, the acquisition of channel state information (CSI) is challenging since IRS usually consists of a massive number of passive elements that have no capabilities of sensing and processing the pilot signals. Although by exploiting the sparsity of the angular domain channel, the huge pilot overhead can be reduced with conventional compressive sensing algorithms, such as approximate message passing (AMP), these algorithms cannot achieve satisfactory channel estimation performance. Based on the learned AMP (LAMP) network, we propose a hypernetwork-assisted LAMP (HN-LAMP) network with dynamic shrinkage parameters to improve the channel estimation accuracy. Furthermore, a recurrent architecture is adopted to reduce the large memory overhead arising from the LAMP network. Simulation results show that the proposed HN-LAMP network can improve the channel estimation accuracy or reduce the computational complexity under satisfactory estimation performance. Moreover, the proposed hypernetwork-assisted recurrent LAMP (HNR-LAMP) architecture can effectively reduce 50% memory overhead by sharing learnable weights.
Subjects
channel estimation | compressive sensing | deep learning | hypernetwork | Intelligent reflecting surface (IRS)
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Type
journal article